Generative AI can help teams assess application portfolios, prepare migration plans and convert code. Cloud migration can also give organizations access to cloud AI services and the data foundations those services need. Neither direction guarantees faster, cheaper or safer outcomes: useful results depend on governed data, architecture fit, cost planning and human review.
How generative AI can help with cloud migration
AI can support migration work, but it should not make consequential migration decisions on its own. Treat generated recommendations as material for architects, security teams, application owners and finance teams to validate.
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Portfolio assessment and planning
A migration assistant can synthesize discovery information and suggest migration plans, including a proposed R-disposition—the recommended treatment of an application, such as rehosting or refactoring. AWS describes a pattern using Amazon Bedrock Agents, action groups and Knowledge Bases to produce migration plans and cost estimates. Its October 15, 2024 article recommends grounding the assistant in organization-specific sources: application-discovery questionnaires, CMDB or discovery-agent output, migration best practices and internal application patterns. Retrieval-augmented generation (RAG) can retrieve relevant material for a response, while customized prompts can help tailor outputs to the task. These are AWS design recommendations, not a guarantee that an answer is accurate or complete. AWS’s migration-assessment pattern
Review the assistant’s output against current application records and dependency evidence. A human should validate architecture, security, compliance and cost assumptions before a plan is approved. Stale CMDB entries or missing dependencies can undermine even a well-designed assistant.
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Code conversion
Generative AI can also help convert application code as part of modernization. AWS reported in an April 29, 2025 announcement that Thailand’s Krungsri reduced migration time by more than 50% compared with manual code conversion using custom agents. That is an AWS-published customer result, not an independently established benchmark or a forecast for other organizations. AWS’s Krungsri announcement
What migration assessment involves—and what the examples show
AI-assisted assessment does not eliminate discovery or application-owner conversations. AWS’s 2024 migration-assessment article says follow-up discussions to review assessment outputs and understand dependencies take approximately two hours per application. It also describes six to eight weeks for portfolio-assessment tasks before application migration begins. These are AWS-published planning figures, not universal timelines; portfolio size, data quality and application complexity affect the work.
Use an AI-generated assessment to focus and organize human review, not to bypass it. Confirm that the inventory is current, check dependencies with application owners, and examine the assumptions behind any disposition or estimate. The source material does not establish an independent cross-provider benchmark for migration speed or cloud cost savings.
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Moving workloads to the cloud can make cloud-hosted AI services and associated data platforms available to an organization. But migration alone does not make data suitable for AI or create business value. Teams still need to decide which data can be used, who can access it, where it may be processed, how long it is retained and how generated outputs are handled.
AWS’s May 2024 guidance recommends treating these questions as part of migration design. It advises involving a Cloud Center of Excellence (CCoE), or equivalent governance body, in generative AI governance; organizing and approving the supporting data architecture; and revisiting migration financial estimates. Willem VanEssendelft, a senior solutions architect on AWS’s Public Sector Migrations team, describes the CCoE as “the connective tissue that brings these activities together under coordinated governance.” AWS guidance on generative AI and migration governance
Govern data and security before enabling AI workflows
Service-level data isolation can be an important control, but it is not a substitute for organization-wide governance. AWS’s guidance highlights questions involving data isolation, sharing, lifecycle, rights, access and jurisdiction. Set policies that cover both information sent to a model and data produced by it; then check that the chosen service and architecture can enforce those policies.
- Data rights: Establish which data the organization is entitled to use and whether internal policies or agreements restrict its use.
- Access: Apply existing identity and access rules to source systems, retrieval indexes, prompts and outputs. Ensure users only receive information they are authorized to see.
- Jurisdiction: Identify applicable geographic requirements for data storage and processing, and confirm the architecture meets them.
- Lifecycle and sharing: Decide how source data, retrieved content and generated outputs are retained, shared and deleted.
- Validation: Require accountable staff to check recommendations before they affect migration, security, compliance or spending decisions.
Revisit the migration budget and make AI spend visible
Generative AI can add development work and service costs beyond a migration’s original estimate. AWS recommends communicating with migration sponsors and budget owners and incorporating expected AI spend into the tagging architecture. Use those practices alongside workload-specific cost modeling; tagging improves attribution but does not predict the bill. Revisit assumptions as the design, model usage and supporting services become clearer.
Prepare people as well as systems
Teams need practical guidance on how to work with AI outputs, where to escalate concerns and which tasks remain subject to review. Google Cloud’s Office of the CTO recommends communication, human-AI collaboration, training, documented AI principles and internal use cases. Google Cloud’s organizational guidance
AWS’s Absa case study describes practical training and migration exercises. AWS reports that Absa upskilled more than 350 employees in cloud, DevOps, AI and machine learning; generated 28 generative AI innovation ideas; recorded a 162% increase in course completion; and migrated two legacy applications. The case study also says 160 employees completed 605 generative AI courses totaling 7,930 learning hours, while its Cloud Incubator involved 215 employees over 12 weeks. These are AWS-published case details, not expected results for other organizations. AWS’s Absa case study
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare cloud migration and AI approaches
There is no neutral provider ranking established by the available evidence. Evaluate options against your workloads, governance requirements and operating model rather than comparing vendor claims as if they were a common benchmark.
Quick Recap
| What to compare | Questions to ask |
|---|---|
| Governance and data controls | How are access, isolation, sovereignty, data rights, retention and generated outputs handled? Which controls are provided by the service, and which remain the organization’s responsibility? |
| Fit with current architecture | Does the approach account for existing workloads, application dependencies and migration patterns? |
| Grounding and integration | Can the assistant use trusted, current organization data, such as discovery records and internal guidance? Microsoft’s Azure article discusses RAG with current, trusted sources. Microsoft’s Azure guidance |
| Cost visibility | Can expected AI services and development costs be modeled and attributed to the relevant workloads or teams? |
| Skills and training | Do staff have documented principles, practical training and a way to review or escalate AI-assisted decisions? |
A practical sequence for migration leaders
- Bring governance into the planning process. Ask the CCoE or equivalent body to address AI data use, architecture, security and accountability as migration plans are developed.
- Organize and approve the data foundation. Identify authoritative discovery and application information, access rules, data rights, jurisdictional constraints and lifecycle requirements.
- Choose a bounded use case. Start with a task such as summarizing discovery records or drafting an assessment for human review, rather than delegating approval.
- Ground and test the assistant. Connect it to approved, maintained organizational information and check outputs for missing dependencies, unsupported assumptions and unsuitable recommendations.
- Model and attribute costs. Update workload-specific estimates and design tagging so AI-related spend can be tracked.
- Train the people doing the work. Explain what AI can assist with, how to verify results and who is accountable for final decisions.
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